Methods for geotemporal fingerprinting

ABSTRACT

Records of geotemporal data for a plurality of users from a database associated with on-line and/or mobile cellular activities are used to generate a geotemporal fingerprint of a user. The geolocation history associated with the user&#39;s on-line and/or mobile cellular activities from one such database can be correlated with a user&#39;s geolocation data from a separate on-line database associated with a different activity. For example, the geolocation history or geotemporal fingerprints associated with on-line and/or mobile cellular activities from one particular database can be correlated with those associated with payment card usage. Such information can be used to authenticate a user, for example, as a security measure for payment card users and issuers, for registrationless credit security applications, and for gathering relevant data for advertising campaigns.

FIELD OF THE INVENTION

The present invention relates to geotemporal fingerprints of a user'srecorded social activity, and, more particularly, to a method forauthenticating a payment card user by generating and comparinggeotemporal fingerprints of users' on-line social activities withrecords of payment card usage.

BACKGROUND OF THE INVENTION

Geolocation data corresponding to various aspects of one's activities isreadily available. For example, many users have a Global PositioningSystem (GPS) associated with their activities in one way or another.Such GPS devices are installed in many automobiles today, either asstand-alone transportable units, or as integrated units positioned inthe dashboard of the automobile as purchased. Additionally, many watchesand smart phones are now available with embedded GPS receivers and theavailability to access a mapping application for providing real-timeglobal positioning and tracking capability.

While it is straightforward to determine the path of a user through theuse of GPS, a temporal history of one's whereabouts can also be gleanedfrom many other sources. Even without a GPS receiver, the location of acell phone on one's person can be roughly estimated from the regularlytimed pings received from the device at a nearest receiver tower. Moredetailed location data is available when a user activates the cell phoneto place a call. Similarly, information about the geolocation historyand habits of users may be recorded from various internet and smartphone applications, such as Facebook®, Twitter®, Foursquare®, and othersocial media applications, including those through which usersvoluntarily and routinely “check-in” or otherwise publish information oftheir physical locations at any particular time.

Another source of geolocation data is payment card usage. Both users andissuers of payment cards are particularly concerned with preventingunauthorized use of payment cards as early as possible. If a paymentcard user opts in to a payment card security system by providing his orher cell phone number, a direct correlation can be made between everypoint-of-sale purchase and the contemporaneous location of the user'scell phone. In co-owned, co-pending patent application Ser. No.13/457,701, filed Apr. 27, 2012, entitled “Method for Providing PaymentCard Security using Registrationless Telecom Geolocation Capture,” byHowe et al., a method for enabling secure payment card usage withoutrequiring a user to enroll or register is provided. A geo-temporalhistory of a payment card user's point-of-sale purchases is tracked tocompare geolocation information for cell phones operated by a mobilenetwork provider to contemporaneous cell phone location in order tomatch cell phone owners to their payment card accounts. A uniqueidentification number can be assigned by the network provider in lieu ofproviding the matching cell phone numbers to the payment card issuer inorder to maintain privacy. The identity of a payment card user can thenbe securely verified by the merchant in future purchases by querying themobile network operator for the location of the payment card user's cellphone number (corresponding to the Identification Number) at the time ofthe purchase.

There is a continuing need in addressing payment card theft and fraud,for authenticating the identity of a card user based on the card user'spoint of sale as well as other usage, without the need for a user toenroll in a credit fraud reporting service. There is also a need, notaddressed in any prior art, for relating databases of users' on-line orother recorded social activity(s) (such as cell phone, Facebook®,Foursquare®, and so-on) to match-up a user's activity recorded for oneservice with a different activity recorded for another service. Suchinformation could be useful, for example, in developing targetedadvertising campaigns.

SUMMARY OF THE INVENTION

The present invention provides a method and system for using thegeolocation history associated with a user's on-line and/or mobilecellular activities to generate a geotemporal fingerprint of the user.The present invention additionally provides a method and system forcorrelating the geolocation history associated with a user's on-lineand/or mobile cellular activities from one particular data base with auser's geolocation data from a separate on-line database associated witha different activity. In particular embodiments, the geolocation historyassociated with on-line and/or mobile cellular activities from oneparticular data base is correlated with geolocation data associated withpayment card usage. Such information can be used, for example, toauthenticate a user, for example, as a security measure for payment cardusers and issuers.

In accordance with one aspect of the present invention, a method forgenerating a geotemporal fingerprint associated with a user from adatabase of geotemporal data recorded for a plurality of users isprovided. The method includes retrieving geotemporal data records forthe plurality of users over a predetermined period of time, where eachof the geotemporal data records includes temporal data and a geolocationindicating a time and place the geotemporal data record is generated,and a User ID associated with the user generating the geotemporal data.

The method further includes determining fields for defining ageotemporal fingerprint associated with each user from the geotemporaldata records for the corresponding User ID, where the fields preferablydistinguish the user from other users associated with the database. Ageotemporal fingerprint associated with each User ID is then generatedbased on the fields.

In an additional aspect, the method further includes identifying andrecording a georegion encompassing the geolocation for each of thegeotemporal data records. The fields can include a primary georegion,where the primary georegion corresponds to the georegion in which eachof the plurality of users is located for a longest time period withinthe predetermined period of time.

A total length of time can then be calculated from the temporal dataassociated with each of the georegions for each User ID, and the primarygeoregion for each User ID identified from the calculated total lengthof time. The method can further include generating the geotemporalfingerprint to include the primary georegion associated with each UserID.

The fields used to generate the geotemporal fingerprints can furtherinclude a secondary georegion, the secondary georegion corresponding tothe georegion in which each of the plurality of users is located for asecond longest time period within the predetermined period of time.Geotemporal fingerprints can then be generated which include thesecondary georegion associated with each User ID.

The geotemporal data records can be cell phone data, generated frompayment card usage, or generated from one or more social networkingactivity.

In other aspects, geotemporal fingerprints generated from a firstdatabase of geotemporal data records in accordance with the inventioncan be used to link a user or user account associated with the firstdatabase to a user account associated with a second database.

Another method of the present invention is provided for linking a firstuser account associated with a first database including geotemporal datarecords for a plurality of users with a second user account associatedwith a second database including geotemporal data records for theplurality of users. Each of the geotemporal data records includestemporal data and a geolocation indicating a time and place thegeotemporal data record is generated and a User ID associated with oneof the plurality of users. The method includes retrieving geotemporaldata records for a first user from the first database generated over apredetermined period of time, each of the geotemporal data recordsretrieved including a first User ID associated with the first user. Themethod further includes determining fields for defining for each user ageotemporal fingerprint associated with the user from the geotemporaldata records with the corresponding User ID. Preferably, the fields arechosen to distinguish the user from other users associated with thedatabase.

The method further includes generating a first geotemporal fingerprintcorresponding to the first User ID based on the fields and retrievinggeotemporal data records from the second database generated for theplurality of users over the predetermined period of time. Each of thegeotemporal data records retrieved from the second database include asecond User ID associated with one of the plurality of users.

A set of second geotemporal fingerprints based on the fields are thengenerated from the geotemporal data records retrieved from the seconddatabase, a second geotemporal fingerprint being generated fromgeotemporal data records comprising the second User ID. The methodfurther includes determining a best match from the set of secondgeotemporal fingerprints generated from the second database to the firstgeotemporal fingerprint, by comparing the geolocation and temporal datain the fields of the first geotemporal fingerprint with the geolocationand temporal data in the fields of the geotemporal data recordsassociated with each of the second geotemporal fingerprints. The secondUser ID corresponding to the best match is linked with the first User IDcorresponding to the first geotemporal fingerprint.

In a further aspect, determining the best match can include identifyinga set of geotemporal data record pairs occurring within a predefinedtime period of one another for each first geotemporalfingerprint/potential match pair; and calculating a parameter indicatinga geographical distance between the geolocations of each of thegeotemporal data record pairs to identify a best match from thepotential matches to the first geotemporal fingerprint.

The parameter can be a great circle distance between the geolocations ofeach of the geotemporal data record pairs, the method further includingcalculating the great circle distance calculated for each of thegeotemporal data record pairs within the set for each potential match.In one aspect, the best match is the one of the potential matches havinga lowest mean great circle distance.

In various additional aspects, the first database includes cell phoneping data and the second database comprises geotemporal data recordsgenerated by payment card usage for the plurality of users.

In other aspects, at least one of the first database and the seconddatabase comprises geotemporal data records generated by a socialnetwork activity. The geolocations and temporal data recorded in thegeotemporal data records generated by the social network activity caninclude internet protocol (IP) addresses, global positioning system(GPS) data, cellular phone ping data, call record details, ortime-stamped textual data.

In addition to the above aspects of the present invention, additionalaspects, objects, features and advantages will be apparent from theembodiments presented in the following description and in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram representation of an embodiment of a method ofthe present disclosure for generating geotemporal fingerprints from cellphone ping data.

FIG. 2 is a flow diagram representation of an embodiment of a method ofthe present disclosure for identifying potential matches to ageotemporal fingerprint generated from one database from geotemporalfingerprints from a second database.

FIG. 3 is a flow diagram representation of an embodiment of a method ofthe present disclosure for identifying a best match to a geotemporalfingerprint generated from one database from geotemporal fingerprintsfrom a second database.

FIG. 4 is a schematic representation of an embodiment of a system forimplementing various embodiments of the methods of the presentdisclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following sections describe exemplary embodiments of the presentinvention. It should be apparent to those skilled in the art that thedescribed embodiments of the present invention provided herein areillustrative only and not limiting, having been presented by way ofexample only. All features disclosed in this description may be replacedby alternative features serving the same or similar purpose, unlessexpressly stated otherwise. Therefore, numerous other embodiments of themodifications thereof are contemplated as falling within the scope ofthe present invention as defined herein and equivalents thereto.

Throughout the description, where items are described as having,including, or comprising one or more specific components, or wheremethods are described as having, including, or comprising one or morespecific steps, it is contemplated that, additionally, there are itemsof the present invention that consist essentially of, or consist of, theone or more recited components, and that there are methods according tothe present invention that consist essentially of, or consist of, theone or more recited processing steps.

It should also be understood that the order of steps or order forperforming certain actions is immaterial, as long as the inventionremains operable. Moreover, two or more steps or actions may beconducted simultaneously.

A “geotemporal fingerprint” is compiled from a series of geolocationsand timestamps that describe a person's travels and activities over aperiod of time, as further described herein.

The term “geolocation” as used herein refers to a user's location ascollected from a cell phone tower or beacon, GPS, or other positionindicators, and can include GPS coordinates, street address, an IPaddress, geo-stamps on digital photographs, smartphone check-in or otherdata, and other location data provided as a result, for example, of atelecommunications or on-line activity of a user. “Regions,” or“georegions,” are geographically defined regions corresponding togroupings of geolocation data, and can refer to cell phone towerbroadcast areas, metropolitan areas, counties, states, or othergroupings made in accordance with the geolocation data.

“Geotemporal” data is temporal and geolocation data (cell phone towerlocation, IP address, GPS coordinates) that is sent, usually along withother information, from a communications device a user is accessing(such as, a cell phone tower, computer, GPS device) to perform a certainactivity at a particular time.

It is understood that, depending on applicable law, cardholders and/orsocial network and telephone users may need to be notified of theprocesses by which various information is obtained, as described herein,by their issuer and/or mobile network operator. In certain cases, theirspecific consent may be needed to include their information in therelevant tables described herein.

The present invention is directed to a method and system for generatinga geotemporal fingerprint from a database of users' activities. Thegeotemporal fingerprint can be used to distinguish a user from otherusers of a database based on criteria associated with the historicalgeolocation activity recorded and stored in the database for each user.The generation of geotemporal fingerprints of a user's activity isuseful for many applications, including for identification of paymentcard fraud without the need for an enrollment or registration process.Although, under applicable laws, even if one's privacy and security isprotected, appropriate specific consent may be warranted.

In one embodiment, a geotemporal fingerprint is generated for users ofcell phones from “ping” data which includes geotemporal data.Optionally, call record data can also be retrieved from records of acellular telephone usage database of a telecommunications serviceprovider. A User ID is preferably associated with each cell phone userand associated with the geotemporal fingerprint for distinguishing theuser from others in the cell phone database.

It is assumed herein that a user travels with his or her cell phone. Asis known among those of ordinary skill in the art, a cell phone “pings”a nearest cell tower at regular intervals, for example, about every fiveminutes. A telecommunications service provider may store thisinformation for a period of time, in some cases, up to about forty-eight(48) hours. The ping data includes a User ID associated with the cellphone from which the ping originates, and a geolocation, for example, acell phone tower ID, which also corresponds to a georegion, or broadcastarea, which is known to contain the user. If a call is made or GPScoordinates requested, however, the telecom provider will have moreprecise positional data, which is stored in call detail records.

Referring to FIG. 1, in accordance with one embodiment 100 of a methodof the present disclosure, the ping data is retrieved for a plurality ofusers/subscribers of a telecommunications service provider 110 over apredetermined period of time, for example, one week, one month, or oneyear. The retrieved ping data is in time sequential order. The ping datais separated into tables, each table corresponding to a different user,or User ID. The ping data records are then reduced or compressed 120.The compression of ping data can be performed as the ping data isreceived from the cell phones, by the service provider, for example, orafter retrieval of stored ping data from the service provider.

To compress the data, for each ping record received, an entry is eithercreated or updated in a table, which contains the following fields: a)User ID; b) Geolocation; and c) Ping Count. The ping records arepreferably sorted into separate tables, one for each User ID, and theentries created or updated in the table corresponding to the particularUser ID.

If ping data for a User ID is imported that includes the samegeolocation as the previous ping entered in the table, the ping countfor that entry is increased by one (1). If a geolocation different thanthe previous time sequential entry, a new entry is created. Each newentry preferably includes a date and time of day. Once the table(s)containing the compressed ping records is populated, all records with acount below a predetermined threshold are preferably removed.

Each of the tables of compressed and filtered data thus provides arecord of the various geolocations pinged by each cell phone userassociated with each User ID, the time of day the cell phone user was atthose locations, and a length of time (from the calculated ping counts)the cell phone user remained at each location. Furthermore, call detailrecords can also be retrieved with records of telephone calls associatedwith each User ID. Whenever a telephone call is made, more exact detailsof the caller's location are recorded. Such details, which include timeof call, duration, and more precise triangulated position data from celltowers, can be used to supplement the ping data.

Referring still to FIG. 1, for each User ID, a so-called geotemporalfingerprint can be generated 150 from the ping data which preferablyuniquely defines each user. For example, for each User ID, georegionscan be identified that correspond with the geolocations 130. Thegeolocations, and the georegions, with the highest count are easilyidentified from the tables of ping data as a “primary geolocation” or“primary region,” respectively 140. In most cases, the primary regionwill be the city or town where the user's residence is located. Thegeolocation with the second highest count can also be identified andmarked as a secondary geolocation. This may indicate a place ofemployment, for example. More than one sequential geolocation entryoccurring within a defined period of time may also be aggregated todefine the primary and secondary georegions for each user ID to identifymajor cities visited. In addition, the call detail records (CDR) oftelephone calls from the phone associated with the User ID may be usedin conjunction with contemporaneous ping data to pinpoint a user'sprimary and secondary, usually a residential and a business address,respectively.

Once a primary and a secondary region are identified, other identifyingcriteria can be defined and ascertained from the ping (and, optionally,the CDR) data and recorded to generate each user's geotemporalfingerprint. For example, the farthest geolocation and associated regionvisited in the last week, month, and/or year, and a geolocation/regionother than the primary and secondary regions visited only once a week,every other week, once a month, or according to any other temporalpattern.

The criteria for defining the geotemporal fingerprints is preferablysufficient to distinguish each geotemporal fingerprint from othergeotemporal fingerprints generated for other User IDs from the samedatabase of telecommunication activity. In addition, in order to match ageotemporal fingerprint generated from the records of one database withgeotemporal fingerprints generated from records of a second database forthe same user, the criteria is preferably adequately defined todetermine a match between the two separate databases of user activity.

It is contemplated that many additional criteria can be set for defininggeotemporal fingerprints 150 using details gleaned from geotemporaldata, such as cellular phone ping data, which reflect a user's travelsand distinguish the user from other users listed in the database. Inadditional embodiments, for example, a weighted statistical distancefunction can be defined as a product of a geographic distance “D” fromthe primary region (or from the secondary region) and a number “P” ofpings over a predetermined period of time. Accordingly, the geotemporalfingerprint can include, for example, a farthest “time-weighted” regionidentified as having the highest P*D value. Multiple variations thereofare also contemplated as within the scope of the invention.

Preferably, the geotemporal fingerprint generated 150 for each userincludes at least the following fields, which are populated using thelocation and temporal information provided by the ping (and, optionally,CDR) data:

-   -   Primary Geolocation    -   Primary Region associated with the Primary Geolocation(s)    -   Secondary Geolocation    -   Secondary Region associated with the Secondary Geolocation(s)    -   Geolocation (Cell Tower) and associated Region Farthest From        Primary Region Visited in the Past Week    -   Geolocation (Cell Tower) and associated Region Farthest From        Primary Region Visited in the Past Month    -   Geolocation (Cell Tower) and associated Region Farthest From        Primary Region Visited in the Past 6 Months    -   Geolocation (Cell Tower) and associated Region Farthest From        Primary Region Visited in the Past Year    -   Geolocation (Cell Tower) and associated Region Second Farthest        From Primary Region Visited in the Past Week    -   Geolocation (Cell Tower) and associated Region Second Farthest        From Primary Region Visited in the Past Month

In one embodiment, the geotemporal fingerprints can also include a timeof day and/or day of the week associated with each of the primary andsecondary location. In addition, the geotemporal fingerprints caninclude an appropriate day of the week or month, and/or time of day, andso on, associated with each of the farthest regions visited.

In various other embodiments, a geotemporal fingerprint is generatedfrom other databases related to other types of user activity, such asone of various types of on-line social networking databases or paymentcard usage. In these embodiments, a geotemporal fingerprint is similarlyformed from the sequential geotemporal data, which can include Beacon orCell Tower IDs or addresses, IP Addresses (for example, from a merchantlocation when a payment card is used, or from a computer/smart phoneutilized by a user accessing social networking databases), or GPSCoordinates, for example. This data will also contain a User ID, ageolocation, and a date and time of day, and may also include a periodof time associated with the use at the geolocation (for example, a timespan over which a user is logged on to an activity and active). One ofordinary skill in the art will recognize that such geolocation data canbe assigned to a geographical region defined by containment according tomethods known in the art. For example, one-dimensional inputs (GPScoordinates) can be assigned to two-dimensional equivalents using, forexample, commercially available Geographic Information System (GIS)software.

Referring to FIG. 2, in particular embodiments, the geotemporalfingerprints for cell phone users (created from large database 210)generated in accordance with the present invention can be compared togeotemporal fingerprints generated from payment card usage (from smallerdatabase 220) to match a payment card user with a cell phone user. Forexample, records of payment card usage can be collected from a paymentcard issuer containing geolocation (point-of-sale) data including amerchant's location, as well as a date and time of purchase. Records ofon-line purchases using one's payment card can also be collected withgeolocation (IP address) and date and time information. Accordingly,geotemporal fingerprints can be created for payment card users, andcorrelated with the geotemporal fingerprints generated for users of cellphones, as described above. Such information can be used, for example,to identify payment card fraud or theft.

It should be appreciated that matching fingerprints generated fromdatabases of two different kinds of activities, one of which provides afairly complete geolocation history (such as cell phone ping data), thanthe other (such as payment card usage) is nontrivial. Similarly,comparing two geotemporal fingerprints derived from two on-lineactivities, such as Facebook® and Foursquare®, is also challengingbecause both represent a partial geolocation history.

Referring again to FIG. 2, in one embodiment of a method of the presentdisclosure 200, a first geotemporal fingerprint 250, which can beassociated with a particular User ID, is selected from a plurality ofgeotemporal fingerprints 230 created from smaller database files 220 forcomparison to every geotemporal fingerprint 240 created from largerdatabase files 210. A field is selected for the comparison 260, which ispreferably a field, such as “farthest region,” which will result in aminimum amount of matches. The matches from the larger database file arethen sorted and recorded in a “Potential_Match” database or table 270for further detailed comparison.

In one embodiment, more than one field is selected for comparison 260.For example, all fields related to a “farthest region” recorded for thelarge geotemporal fingerprints 240 can be compared to those in the firstgeotemporal fingerprint 250. One or more closest-matching largegeotemporal fingerprints can be selected by incrementing a counterassociated therewith by one (1) for every ‘farthest region’ in commonwith the first geotemporal fingerprint 250. The larger geotemporalfingerprint(s) 240 with a highest count are then sorted and recorded inthe “Potential_Match” database 270 for further detailed comparison tothe first geotemporal fingerprint.

Referring to FIG. 3, for further comparison, the uncompressed (large)geotemporal data 315 corresponding to the geotemporal fingerprints inthe Potential_Match database is preferably accessed 310, and theuncompressed (partial or small) geotemporal data 325 corresponding tothe first geotemporal fingerprint is preferably accessed 320, forgeotemporal comparison. Because the geotemporal data are generated bydifferent sources, however, the records can not be expected to exactlymatch. In order to determine a common user associated with two differentdatabases, therefore, matching algorithms are needed to identifygeotemporal records associated with a user from one database whichclosely match in time and location to geotemporal records with a userfrom another database.

In one embodiment, each geotemporal record from the smaller geotemporaldata source 325 used to form the first fingerprint is compared to afirst one of the larger geotemporal data Potential_Match files toidentify a set of nearest-time matched geotemporal pairs between thefirst one of the list of Potential_Match geotemporal file and thesmaller geotemporal data source file 325. The nearest-time matchedgeotemporal pairs are within a predefined time period of one another. Ifno records in the larger Potential_Match file are within a predefinedtime period of one of the records of the smaller geotemporal data sourcefile 325, then the data point from the larger geotemporal data sourcefile is simply ignored. If no nearest-time matched records areidentified for a particular Potential_Match file, that file is droppedfrom the list of Potential Matches. In certain embodiments, thepredefined time period for comparing the records from thePotential_Match files to the smaller geotemporal data file can beadjusted and the procedure repeated for each file in the Potential_Matchlist.

The number of geotemporal records in the larger data file that occurwithin the predefined time period of a geotemporal record from thesmaller data file are also preferably added up and entered in thePotential_Matches database for the record corresponding to thatgeotemporal fingerprint, and used to confirm a temporal match betweenrecords.

Once one or more pairs of nearest-time matching records are identifiedfor each pair of Potential_Match/smaller ping files 330, a great circledistance between the two geolocation beacons (cell tower/IP address)associated with the nearest-time matched pairs of geotemporal recordsfrom the two databases is calculated and preferably stored 340 for eachPotential_Match in the Potential_Match database or table 270.

In one embodiment, all of the great circle distances for each pair ofnearest-time matching records are then added and entered in thePotential_Matches database 270 as a cumulative_great_circle_distancefield for each potentially matching geotemporal fingerprint from thePotential_Matches database. Additionally, or alternatively, the mean,median, mode, min, and/or max great circle distance can be calculatedfrom the pairs of nearest-time matching records for each potentiallymatching fingerprint 350. The geotemporal fingerprint from the larger(second) database in the Potential_Matches database with the lowest meangreat circle distance, or other chosen metric 350, is preferablydetermined to be the best match 360.

The foregoing process described for the first (partial) geotemporalfingerprint 250 with reference to FIGS. 2 and 3 can be repeated forevery one of the geotemporal fingerprints 230 created from the smallerdatabase files 220.

In other embodiments, the geotemporal fingerprint from the larger(second) database in the Potential_Matches database with the lowestmedian or mode or overall minimum great circle distance is used todetermine the best match. Alternatively, one of skill in the art willrecognize that a combination of these and other metrics useful incomparing the location and time data can be used to determine the bestmatch.

The method of the present invention can be readily applied to acomparison of ping data from cell phone usage and payment card usage. Inthis case, the uncompressed ping data from the cell phone database iscompared to the geotemporal data from payment card usage, which does notneed to be initially compressed as described in FIG. 1. The payment cardgeotemporal data can comprise IP addresses from point-of-sale merchantsand from user's on-line purchase activity.

For example, where IP addresses are included in payment stream data, itis possible to match IP addresses to payment card data in astraightforward manner to provide a more complete geotemporal history ofa cardholder's usage.

In another embodiment, geotemporal data from on-line social networkingactivities is used to generate geotemporal fingerprints from geolocationand temporal records of usage of the on-line social network, whichcontain geolocation data in the form of internet addresses, and/oractual street address information. The geotemporal fingerprint(s)associated with one or more of a user's on-line social activities can becorrelated with the geotemporal fingerprints of the payment card usersaccording to the methods of the present disclosure, for use inidentifying payment card theft, for example.

For example, it is generally known that as much as 50% of users ofeCommerce sites are logged onto Facebook® contemporaneously with thepurchase. Facebook® records the sites visited while a user is logged in.This information can then be matched to cardholder accounts using spendhistory. In addition to aiding in prevention of payment card fraud byproviding another way to provide credit fraud protection without theneed for registration, this information can be used to create a“purchase verification” service for the payment provider. In variousadditional embodiments, Facebook® data can be enhanced withconfirmations of purchase for use in Facebook's advertising return ofinvestment (ROI) calculations.

Another example of an on-line activity which can provide geotemporaldata is Instagram®. This and similar services contain timestamp andgeolocation metadata at the time a user uploads his or her photos (orany other media, video, or data having timestamp and location data). Forusers with substantial picture histories, correlations can be drawnbetween the geolocations of payment card usage and the photo geolocationand temporal data according to the methods of the present invention.

Similarly, various on-line “check-in” apps provide a check-in featuretied to a geolocation and timestamp, and thus can also be used tocreate, or supplement, geotemporal data for generating geotemporalfingerprints of users' on-line activity. If a reasonable number ofcheck-ins are in proximity to purchases, the geolocation data generatedby such check-in apps can be correlated with payment card usage.

In other embodiments, geotemporal fingerprints generated for users of afirst database recording a particular activity, such as an on-linesocial networking activity, can be correlated with geotemporalfingerprints generated for users of a second database, which could beanother on-line social networking activity, to identify users common toboth databases. Such information can be used, for example, to identifycommon attributes and activities of a group of users, associated withthe correlated data, and could be useful, for example, in developingtargeted advertising campaigns.

In another embodiment, a composite geotemporal fingerprint can be formedfor a user from more than one on-line social networking activity to forma more complete geotemporal fingerprint, which can then be used tocompare with payment card usage, for example. The composite geotemporalfingerprint can be formed from matching fingerprints identified usingthe methods of the present disclosure. Accordingly, the compositefingerprint can include or merge a first fingerprint for a user formedfrom a first database, and a second fingerprint for the user formed froma second database. Otherwise, the composite geotemporal fingerprint maybe formed from records from the two databases of networking activitiesotherwise known to belong to the same user.

As should be clear to those of skill in the art, the various embodimentsof the methods of the present invention are implemented via computersoftware or executable instructions or code. Referring to FIG. 4, asystem 400 for implementing the methods of the present disclosureincludes at least a processor 410 including a Central Processing Unit(CPU), memory 420, and interface hardware 430 for connecting to externalsources of data 435, for example, via the Internet 440.

Any of the raw, filtered, or generated tables and other databasesdescribed herein may be stored in an external memory 435, and accessedremotely, for example, via the Internet or other means, or may be storedin one of a number of local memory devices 420 of a system 400 forimplementing the methods of the present disclosure.

The system 400 can be a computer with display 450 and input keypad orkeyboard 460, and a media drive 465, or a handheld or other portabledevice with a display, keypad, memory, processor, network interface, anda media interface such as a flash drive. The memory 420 includescomputer readable memory accessible by the CPU for storing instructionsthat when executed by the CPU 410 causes the processor 410 to implementthe steps of the methods described herein. The memory 420 can includerandom access memory (RAM), read only memory (ROM), a storage deviceincluding a hard drive, or a portable, removable computer readablemedium, such as a compact disk (CD) or a flash memory, or a combinationthereof. The computer executable instructions for implementing themethods of the present invention may be stored in any one type of memoryassociated with the system 400, or distributed among various types ofmemory devices provided, and the necessary portions loaded into RAM, forexample, upon execution.

In one embodiment, a non-transitory computer readable product isprovided, which includes a computer readable medium, for example,computer readable medium 470 shown in FIG. 4 that can be accessed by theCPU via media drive 465, for storing computer executable instructions orprogram code for performing the method steps described herein. It shouldbe recognized that the components illustrated in FIG. 4 are exemplaryonly, and that it is contemplated that the methods described herein maybe implemented by various combinations of hardware, software, firmware,circuitry, and/or processors and associated memory, for example, as wellas other components known to those of ordinary skill in the art.

While the invention has been particularly shown and described withreference to specific embodiments, it should be apparent to thoseskilled in the art that the foregoing is illustrative only and notlimiting, having been presented by way of example only. Various changesin form and detail may be made therein without departing from the spiritand scope of the invention. Therefore, numerous other embodiments arecontemplated as falling within the scope of the present invention asdefined by the accompanying claims and equivalents thereto.

What is claimed is:
 1. A method for generating a geotemporal fingerprintfor a user from a database of geotemporal data recorded for a pluralityof users, the method comprising: retrieving geotemporal data records forthe plurality of users generated over a predetermined period of time,each of the geotemporal data records comprising temporal data and ageolocation indicating a time and place the geotemporal data record isgenerated and a User ID associated with one of the plurality of users;determining fields for defining for each user a geotemporal fingerprintassociated with the user from the geotemporal data records comprisingthe corresponding User ID, the fields distinguishing the user from otherusers associated with the database; and generating the geotemporalfingerprint associated with each User ID based on the fields.
 2. Themethod in accordance with claim 1, further comprising identifying andrecording a georegion encompassing the geolocation for each of thegeotemporal data records, wherein the fields comprise at least a primarygeoregion, the primary georegion corresponding to the georegion in whicheach of the plurality of users is located for a longest time periodwithin the predetermined period of time.
 3. The method in accordancewith claim 2, further comprising calculating a total length of time fromthe temporal data associated with each of the georegions for each UserID, identifying the primary georegion for each User ID from thecalculated total length of time, and generating the geotemporalfingerprint comprising the primary georegion associated with each UserID.
 4. The method in accordance with claim 3, wherein the fields furthercomprise a secondary georegion, the secondary georegion corresponding tothe georegion in which each of the plurality of users is located for asecond longest time period within the predetermined period of time, themethod further comprising identifying the secondary georegion for eachUser ID from the calculated total length of time, and generating thegeotemporal fingerprint further comprising the secondary georegionassociated with each User ID.
 5. The method in accordance with claim 1,further comprising identifying and recording a georegion encompassingthe geolocation for each of the geotemporal data records, and whereinthe fields comprise a farthest georegion recorded on a periodic basiswithin the predetermined period of time, the method further comprisingidentifying the farthest georegion recorded on the periodic basis foreach User ID from the temporal data associated with each georegion, andgenerating the geotemporal fingerprint associated with each User IDcomprising the farthest georegion recorded on the periodic basis.
 6. Themethod in accordance with claim 1, wherein the geotemporal data recordscomprise cell phone ping data.
 7. The method in accordance with claim 1,wherein the geotemporal data records are generated from payment cardusage.
 8. The method in accordance with claim 1, wherein the geotemporaldata records are generated from at least one on-line social networkingactivity.
 9. A method for linking a first user account associated with afirst database comprising geotemporal data records for a plurality ofusers with a second user account associated with a second databasecomprising geotemporal data records for the plurality of users, each ofthe geotemporal data records comprising temporal data and a geolocationindicating a time and place the geotemporal data record is generated anda User ID associated with one of the plurality of users, the methodcomprising: retrieving geotemporal data records for a first user fromthe first database generated over a predetermined period of time, eachof the geotemporal data records retrieved comprising a first User IDassociated with the first user; determining fields for defining for eachuser a geotemporal fingerprint associated with the user from thegeotemporal data records comprising the corresponding User ID, thefields distinguishing the user from other users associated with thedatabase; generating a first geotemporal fingerprint corresponding tothe first User ID based on the fields; retrieving geotemporal datarecords from the second database generated for the plurality of usersover the predetermined period of time, each of the geotemporal datarecords retrieved from the second database comprising a second User IDassociated with one of the plurality of users; generating a set ofsecond geotemporal fingerprints based on the fields from the geotemporaldata records retrieved from the second database, a second geotemporalfingerprint being generated from geotemporal data records comprising thesecond User ID; and determining a best match from the set of secondgeotemporal fingerprints generated from the second database to the firstgeotemporal fingerprint, comprising comparing the geolocation andtemporal data in the fields of the first geotemporal fingerprint withthe geolocation and temporal data in the fields of the geotemporal datarecords associated with each of the second geotemporal fingerprints, thesecond User ID corresponding to the best match being linked with thefirst User ID corresponding to the first geotemporal fingerprint. 10.The method according to claim 9, further comprising identifying andrecording a georegion encompassing the geolocation for each of thegeotemporal data records retrieved from the first database and thesecond database, wherein the fields comprise at least a primarygeoregion, the primary georegion corresponding to the georegion in whicheach of the plurality of users is located for a longest time periodwithin the predetermined period of time, the method further comprisingcalculating a total length of time from the temporal data associatedwith each of the georegions for each User ID, identifying the primarygeoregion for each User ID from the calculated total length of time, andgenerating the geotemporal fingerprint comprising the primary georegionassociated with each User ID.
 11. The method according to claim 10,further comprising identifying geotemporal fingerprints from the setgenerated from the second database for which the georegion identified asthe primary georegion is the same as the georegion identified as theprimary georegion of the first geotemporal fingerprint as potentialmatches; and determining a best match further comprises comparing thegeolocation and temporal data of the geotemporal data records in thefields of the first geotemporal fingerprint with the geolocation andtemporal data in the fields of the geotemporal data records associatedwith each of the potential matches.
 12. The method of claim 11, whereindetermining a best match further comprises: identifying a set ofgeotemporal data record pairs occurring within a predefined time periodof one another, each pair comprising a geotemporal data recordcorresponding to the first User ID and a geotemporal data recordcorresponding to one of the second User IDs corresponding to one of thepotential matches; and calculating a parameter indicating a geographicaldistance between the geolocations of each of the geotemporal data recordpairs to identify the best match from the potential matches to the firstgeotemporal fingerprint.
 13. The method of claim 12, wherein theparameter is a great circle distance between the geolocations of each ofthe geotemporal data record pairs, the method further comprisingcalculating the great circle distance calculated for each of thegeotemporal data record pairs within the set for each potential match.14. The method of claim 13, wherein the best match is the one of thepotential matches having a lowest mean great circle distance.
 15. Themethod of claim 9, wherein the first database comprises cell phone pingdata and the second database comprises geotemporal data recordsgenerated by payment card usage for the plurality of users.
 16. Themethod of claim 9, wherein at least one of the first database and thesecond database comprises geotemporal data records generated by a socialnetwork activity.
 17. The method of claim 16, wherein the geolocationsand temporal data recorded in the geotemporal data records generated bythe social network activity comprise at least one of internet protocol(IP) addresses, global positioning system (GPS) data, cellular phoneping data, call record details, and time-stamped textual data.
 18. Themethod of claim 16, wherein the first database comprises geotemporaldata records generated by the social network activity and the seconddatabase comprises geotemporal data records generated by payment cardusage for the plurality of users.
 19. The method of claim 10, whereinthe fields for generating each geotemporal fingerprint further comprisea secondary georegion, the secondary georegion corresponding to thegeoregion in which each of the plurality of users is located for asecond longest time period within the predetermined period of time, themethod further comprising identifying the secondary georegion for eachUser ID from the calculated total length of time, and generating thegeotemporal fingerprint further comprising the secondary georegionassociated with each User ID.